



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case     :int			       =logging.get_logger(__name__)

__snake_case     :Dict			       ={
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     int								=     'xlm-roberta-xl'


				def __init__(							self      :  Optional[int]						, __UpperCamelCase      :  Union[str, Any]=250_880						, __UpperCamelCase      :  Optional[int]=2_560						, __UpperCamelCase      :  Optional[int]=36						, __UpperCamelCase      :  Union[str, Any]=32						, __UpperCamelCase      :  int=10_240						, __UpperCamelCase      :  Optional[Any]="gelu"						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  Union[str, Any]=514						, __UpperCamelCase      :  List[str]=1						, __UpperCamelCase      :  Tuple=0.0_2						, __UpperCamelCase      :  Any=1e-05						, __UpperCamelCase      :  int=1						, __UpperCamelCase      :  int=0						, __UpperCamelCase      :  int=2						, __UpperCamelCase      :  List[str]="absolute"						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  str=None						, **__UpperCamelCase      :  Optional[int]						, )		->     List[Any]:
											super().__init__(pad_token_id=__UpperCamelCase						, bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    hidden_act
											A      						=    intermediate_size
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    type_vocab_size
											A      						=    initializer_range
											A      						=    layer_norm_eps
											A      						=    position_embedding_type
											A      						=    use_cache
											A      						=    classifier_dropout





class        lowerCAmelCase__					(  _lowerCamelCase       ):


				@property
				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Mapping[str, Mapping[int, str]]:
											if self.task == "multiple-choice":
																		A      						=    {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		A      						=    {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]       )

"""simple docstring"""



def 			_UpperCamelCase						(    _A = 1_0_0_0   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = 1, 1
    _UpperCAmelCase        = 2
    while True:
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = fa + fa
        _UpperCAmelCase     ,_UpperCAmelCase        = fa, f
        index += 1
        for _ in str(_A   ):
            i += 1
        if i == n:
            break
    return index


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))